UGR Blair Creek Ltd. (UGR) developed an empirical equation to predict Montney horizontal well production based on several completion parameters: lateral length, number of fracture stages, perforation clusters per fracture stage and fluid volume. We used multivariate regression analysis to determine how the completion parameters influence observed production rates. Our best model is valid for fracturing treatments using slick-water and all of the wells in this study are located in British Columbia. The Montney is an expansive play and thousands of wells will be drilled in the future, so the results of this analysis and application of this technique to expanded data sets should help with completion optimization and can have a significant impact on well performance and/or completion cost.

We reviewed completion reports on 425 wells to extract several parameters, including lateral length, number of fracture stages, number of perforation clusters per stage, fluid volume, fluid type and sand volume. Public production data were used to obtain monthly production volumes for each well. We performed multivariate linear regression analysis, simultaneously regressing all of the completion variables against the average production during the best year of production.

We found that the number of fracture stages and the number of perforation clusters per stage are the most important completion parameters for predicting well performance. As expected, an increase in either of these variables will increase the production volume of the best year of production. Adding one fracture stage adds roughly 200 Mcf/D (5,600 m3/d) and adding one perforation cluster per stage adds roughly 250 Mcf/D (7,400 m3/d) to the best year of production. The impact of additional fracture stages or perforation clusters was more pronounced on wells using slick-water fracture fluids than treatments using more complex fluid systems. The amount of sand used in the fracture treatments may also have a significant positive impact on production, but there is more uncertainty than with the number of fracture stages and perforation clusters per stage. Lateral length and fluid volume were only marginally important in predicting production volumes. Only by using multivariate regression (all variables are simultaneously combined in a single regression model) were we able to discern the impact of individual variables.

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